Differences

This shows you the differences between two versions of the page.

--- project [2019/09/04 14:22] – calden
+++ project [2019/11/27 15:45] (current) – calden
@@ Line 11: / Line 11: @@
 ===Component Deadlines===
-    * White Paper
+    * White Paper - September 23rd
-    * Proposal
+    * Proposal - October 9th
-    * Site Visit
+    * Site Visit - November 4th/6th (in lab)
-    * Demo
+    * Demo - November 20th, 25th, 27th, December 2nd (in class)
-    * Final Report
+    * Final Report - original deadline: December 2nd, revised deadline: December 13th
 ===Engineering Stream===
@@ Line 58: / Line 58: @@
 Note that if a student would like to claim one of these models for their project, they are encouraged to speak to the instructor early to avoid duplication of work with another student.
-  * Discriminant Saliency - Gao et al., 2008, [[https://jov.arvojournals.org/article.aspx?articleid=2193585|Paper Link]]. Code was released as a binary, and does not appear to run anymore.
+  * Discriminant Saliency - Gao et al., 2008, [[https://jov.arvojournals.org/article.aspx?articleid=2193585|Paper Link]]. Code was released as a binary, and does not appear to run anymore. **Project selected by a student.**
-  * Discriminative Correlation Filter with Channel and Spatial Reliability - Lukežič et al., 2017, [[http://openaccess.thecvf.com/content_cvpr_2017/html/Lukezic_Discriminative_Correlation_Filter_CVPR_2017_paper.html|Paper Link]]. Code was released for MATLAB, and could be converted to Python or C++. [[https://github.com/alanlukezic/csr-dcf|GitHub Repository of MATLAB Code]]
+  * Discriminative Correlation Filter with Channel and Spatial Reliability - Lukežič et al., 2017, [[http://openaccess.thecvf.com/content_cvpr_2017/html/Lukezic_Discriminative_Correlation_Filter_CVPR_2017_paper.html|Paper Link]]. Code was released for MATLAB, and could be converted to Python or C++. [[https://github.com/alanlukezic/csr-dcf|GitHub Repository of MATLAB Code]] - **Project selected by a student.**
-  * Learning Background-Aware Correlation Filters for Visual Tracking - Galoogahi et al., 2017, [[http://openaccess.thecvf.com/content_iccv_2017/html/Galoogahi_Learning_Background-Aware_Correlation_ICCV_2017_paper.html|Paper Link]]. Code was released for MATLAB, and could be converted to Python or C++. [[http://www.hamedkiani.com/bacf.html|Project page with link to code]]
+  * Learning Background-Aware Correlation Filters for Visual Tracking - Galoogahi et al., 2017, [[http://openaccess.thecvf.com/content_iccv_2017/html/Galoogahi_Learning_Background-Aware_Correlation_ICCV_2017_paper.html|Paper Link]]. Code was released for MATLAB, and could be converted to Python or C++. [[http://www.hamedkiani.com/bacf.html|Project page with link to code]] - **Project selected by a student.**
-  * Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors - Huang et al., 2016, [[https://www.mdpi.com/2072-4292/8/6/483|Paper Link]]. Model appears to have been released without code.
+  * Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors - Huang et al., 2016, [[https://www.mdpi.com/2072-4292/8/6/483|Paper Link]]. Model appears to have been released without code. - **Project selected by a student.**
   * Compositional Model Based Fisher Vector Coding for Image Classification - Liu et al., 2017, [[https://ieeexplore.ieee.org/abstract/document/7812753|Paper Link]]. Model appears to have been released without code.
+  * Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera - Chen et al., 2017 [[http://jtl.lassonde.yorku.ca/wp-content/uploads/2017/02/pfr_paper_crv2017.pdf|Paper Link]]. Model released without code, but the dataset is publicly available: [[http://jtl.lassonde.yorku.ca/2017/02/person-following/|Project page]]. Note that this is a robotics paper; only the vision component would be expected to be completed, and evaluation on existing data would be sufficient.
+  * Early Recurrence Improves Edge Detection - Shi et al., 2013, [[http://www.bmva.org/bmvc/2013/Papers/paper0022/paper0022.pdf|Paper Link]]. Model appears to have been released without code. **Project selected by a student.**
 ==A Prior Example from the Literature==
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   * Adaptive stereo vision: In our study of stereopsis, we will learn that a useful strategy is to begin our estimation procedures with coarse image data (e.g., imagery with low spatial resolution) and subsequently refine our solution through systematic incorporation of more refined image data (e.g., imagery with higher spatial resolution). We will refer to this paradigm as course-to-fine refinement. An interesting question that arises in this paradigm is how to decide on the level of refinement that is appropriate for a given image or even a given image region. For this project, the student will explore methods for automatically adapting the coarse-to-fine refinement of stereo estimates based on the input binocular image data and implement as well as test at least one such procedure.
-  * Primitive event recognition: The combinatorics of some approaches prove challenging either from a required hardware perspective or to achieve real-time processing. For timely or efficient processing, it is critical that early operations begin to recognize significant patterns in this data. For example, when processing a video sequence, it might be desirable to make distinctions regarding those portions of the imagery that correspond to moving objects so that subsequent processing might attend to such regions. For this project, the student will suggest and investigate a set of simple, dynamic patterns or events that it might be advantageous to support through early processing (e.g., what is moving, what is not, what is noise, etc.). The student will develop an analysis that shows how the patterns can be distinguished on the basis of simple vision operations as well as implement and test corresponding algorithms.
+  * Primitive event recognition: The combinatorics of some approaches prove challenging either from a required hardware perspective or to achieve real-time processing. For timely or efficient processing, it is critical that early operations begin to recognize significant patterns in this data. For example, when processing a video sequence, it might be desirable to make distinctions regarding those portions of the imagery that correspond to moving objects so that subsequent processing might attend to such regions. For this project, the student will suggest and investigate a set of simple patterns or events that it might be advantageous to support through early processing (e.g. for videos, what is moving, what is not, what is noise, etc.). The student will develop an analysis that shows how the patterns can be distinguished on the basis of simple vision operations as well as implement and test corresponding algorithms.
   * Module combination: Typically, a solitary computer vision module provides incomplete information about the world; however, taken in tandem (two or more) modules might combine forces to provide a better understanding of the environment. For example, binocular stereo typically performs best in regions with well define features or texture patterns, but poorly in smoothly shaded regions; in contrast, shape from shading can perform reasonably in smoothly shaded regions, but less well in highly textured regions. For this project, students will select a pair of complimentary vision modules (e.g., stereo and shading, feature-based and area-based image correspondence/matching for stereo, binocular stereo and motion, etc.) and study how they can be combined in an advantageous fashion. The student will develop an analysis that shows how the modules can be combined as well as implement and test corresponding algorithms.